Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users. Recent work has investigated the role of humans in enhancing AutoML functionality throughout a standard ML workflow. However, it is also critical to understand how users adopt existing AutoML solutions in complex, real-world settings from a holistic perspective. To fill this gap, this study conducted semi-structured interviews of AutoML users (N = 19) focusing on understanding (1) the limitations of AutoML encountered by users in their real-world practices, (2) the strategies users adopt to cope with such limitations, and (3) how the limitations and workarounds impact their use of AutoML. Our findings reveal that users actively exercise user agency to overcome three major challenges arising from customizability, transparency, and privacy. Furthermore, users make cautious decisions about whether and how to apply AutoML on a case-by-case basis. Finally, we derive design implications for developing future AutoML solutions.

翻译：自动化机器学习（AutoML）旨在使普通人也能掌握机器学习技术。近期研究探讨了人在标准ML工作流程中增强AutoML功能的作用。然而，从整体视角理解用户如何在复杂现实环境中采用现有AutoML解决方案同样至关重要。为填补这一空白，本研究对19名AutoML用户进行了半结构化访谈，重点了解：(1)用户在真实实践中遇到的AutoML局限性，(2)用户应对这些局限性的策略，以及(3)这些局限性与应对策略如何影响其使用AutoML。研究结果表明，用户积极行使自主权以克服可定制性、透明性和隐私性三大挑战。此外，用户会根据具体案例谨慎决定是否及如何应用AutoML。最后，我们提出了对未来AutoML解决方案的设计启示。